· What features of current ontologies, or related inference capabilities, are or are not particularly useful for federation and integration?
Overly large models which are not federated, overly complex logics which are not decidable. People want to examine large amounts of data very quickly. This is not an academic exercise in this world. Things need to be fast with very straight-forward and intuitive front ends.
K - > The close integration with a co-produced front end is an innovative practice. Involving the information consumer in the design works for me.
· Are upper and/or mid ontologies useful and/or necessary for semantic integration and federation?
Yes, very useful, but need to be federated and capable of expansion and integration with other data models. The idea of making SMEs accept someone else’s language and terminologies is simply not realistic. Good semantic systems I have seen have highly reusable components and are extensible without a lot of extra investment on the part of the company.
· Where do existing ontological languages such as OWL, CL or basic FOL meet or not meet the needs of federation and integration?
This depends on what you want to do. My company (Orbis Technologies uses different logics for different applications). The one mentioned above utilizes Object Logic and it works very nicely.
· What is the relationship between conceptual modeling and and/or logical models and ontologies?
I’m not sure there always is one – other than to say I understand conceptual models to be epistemic models, probably linked to specific perspectives, logic models are how the reasoner is going to perform (but I am not going to pretend to hold to this distinction). One thing everyone in industry needs to be aware of is explicitly capturing different kinds of data and having different parts of the models that can perform different tasks. Real world objects (documents, equipment, products, personnel, etc.) should be placed in real world domain ontologies. Information entities (metadata, ontologies about data objects or measurements, etc.) should be properly placed in their own domains. Entities extracted from NLP processing should be placed in its proper spot. Inferred relations and autoclassifications should be put in their spots, and so on. This way people (and reasoners) can better interpret and use the data. Doing this little bit of metaphysical cleanup is useful for moving forward as projects continue to evolve and grow. Clients can be educated into this systems approach for the ontology and often will see the value once the system begins producing good results.
· What tools ,standards or other capabilities are missing?
Scalability is a big issue still for many ontology solutions. This often can be fixed, but not through ontologies per se. One can enhance the system on several levels. First, at the data source level itself (we sometimes can clean up the tables themselves, normalize DBs, etc.). One can federate out the models at various layers to better handle queries and rules. One can use things like Hadoop/MapReduce approaches to be able to thread queries and algorithms so as to only utilize those parts of the models needed for certain reports, queries or investigations. Again, ontologies themselves do not really provide all of the answers here. This is a systems approach – sometimes one even cheats on the front end by performing lazy loading, etc., to make the experience seem faster than it is because things are loading in the background.
K - > I agree.
· If ontologies solve this problem, why isn’t the problem solved?
I think my position is clear by now – ontologies do not solve many of these problems. This is like asking “if I have a wiring diagram that shows the entire structure of my car, why does it not fix itself?” The mechanic can use that diagram to solve problems easier than poking around blindly, but the ontology is just a map or a model of the classes, attributes, time stamps, events, etc., that people care about within a certain set of domains (I’m sure I will get flamed by my fellow philosophers for making this kind of crude statement – and I get the value of deep metaphysics). The point is the ontology is NOT an end-to-end system, it is part of the puzzle. It provides an important backbone for all of the things I list above, but it does not provide the solution in isolation.
The intent is not to answer all of these but to spur discussion on topics of your choosing.
Regards,
Cory Casanave